Family Health Support Centers: challenges and opportunities from the perspective of primary care professionals in the city of Sao Paulo, Brazil

Family Health Support Centers (NASF) were created in Brazil to increase the case-resolution capacity of primary healthcare. Prior to their implementation in the West Side of the city of Sao Paulo, Brazil, a series of workshops were held for primary healthcare professionals to prepare a proposal for such centers. Hermeneutic analysis was used to study the transcribed material. The thematic categories were: role, constitution, and functioning of the NASF, relationship with family health teams, and interdisciplinarity. The participants' expected the NASF to be an empowering device for comprehensiveness of care, intervening in an existing culture of unnecessary referrals while fostering linkage with other levels of care. The participants also expected the NASF to contribute to the discussion on health professionals' training and stimulating reflection with policy-makers on health indicators based exclusively on the number of consultations. These indicators fail to reflect the impact on the services' activities and the quality of care offered to the population in the coverage area.

Relationship among social support, treatment adherence and metabolic control of diabetes mellitus patients

This cross-sectional and quantitative study aimed to analyze the relationship among social support, adherence to non-pharmacological (diet and physical exercise) and pharmacological treatments (insulin and/or oral anti-diabetic medication) and clinical and metabolic control of 162 type 2 diabetes mellitus patients. Data were collected through instruments validated for Brazil. Social support was directly correlated with treatment adherence. Adherence to non-pharmacological treatment was inversely correlated with body mass index, and medication adherence was inversely correlated with diastolic blood pressure. There were no associations between social support and clinical and metabolic control variables. Findings indicate that social support can be useful to achieve treatment adherence. Studies with other designs should be developed to broaden the analysis of relations between social support and other variables.

Matrix-vector multiplication and triangular linear solver using GPGPU for symmetric positive definite matrices derived from elliptic equations

The modern GPUs are well suited for intensive computational tasks and massive parallel computation. Sparse matrix multiplication and linear triangular solver are the most important and heavily used kernels in scientific computation, and several challenges in developing a high performance kernel with the two modules is investigated. The main interest it to solve linear systems derived from the elliptic equations with triangular elements. The resulting linear system has a symmetric positive definite matrix. The sparse matrix is stored in the compressed sparse row (CSR) format. It is proposed a CUDA algorithm to execute the matrix vector multiplication using directly the CSR format. A dependence tree algorithm is used to determine which variables the linear triangular solver can determine in parallel. To increase the number of the parallel threads, a coloring graph algorithm is implemented to reorder the mesh numbering in a pre-processing phase. The proposed method is compared with parallel and serial available libraries. The results show that the proposed method improves the computation cost of the matrix vector multiplication. The pre-processing associated with the triangular solver needs to be executed just once in the proposed method. The conjugate gradient method was implemented and showed similar convergence rate for all the compared methods. The proposed method showed significant smaller execution time.

Non-Linear Analysis and Design of Synchronous Bearingless Multiphase Permanent Magnet Machines and Drives

A two-dimensional model to analyze the distribution of magnetic fields in the airgap of a PM electrical machines is studied. A numerical algorithm for non-linear magnetic analysis of multiphase surface-mounted PM machines with semi-closed slots is developed, based on the equivalent magnetic circuit method. By using a modular structure geometry, whose the basic element can be duplicated, it allows to design whatever typology of windings distribution. In comparison to a FEA, permits a reduction in computing time and to directly changing the values of the parameters in a user interface, without re-designing the model. Output torque and radial forces acting on the moving part of the machine can be calculated. In addition, an analytical model for radial forces calculation in multiphase bearingless Surface-Mounted Permanent Magnet Synchronous Motors (SPMSM) is presented. It allows to predict amplitude and direction of the force, depending on the values of torque current, of levitation current and of rotor position. It is based on the space vectors method, letting the analysis of the machine also during transients. The calculations are conducted by developing the analytical functions in Fourier series, taking all the possible interactions between stator and rotor mmf harmonic components into account and allowing to analyze the effects of electrical and geometrical quantities of the machine, being parametrized. The model is implemented in the design of a control system for bearingless machines, as an accurate electromagnetic model integrated in a three-dimensional mechanical model, where one end of the motor shaft is constrained to simulate the presence of a mechanical bearing, while the other is free, only supported by the radial forces developed in the interactions between magnetic fields, to realize a bearingless system with three degrees of freedom. The complete model represents the design of the experimental system to be realized in the laboratory.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).

Classification algorithms for Intelligent Transport Systems

Intelligent Transport Systems (ITS) consists in the application of ICT to transport to offer new and improved services to the mobility of people and freights. While using ITS, travellers produce large quantities of data that can be collected and analysed to study their behaviour and to provide information to decision makers and planners. The thesis proposes innovative deployments of classification algorithms for Intelligent Transport System with the aim to support the decisions on traffic rerouting, bus transport demand and behaviour of two wheelers vehicles. The first part of this work provides an overview and a classification of a selection of clustering algorithms that can be implemented for the analysis of ITS data. The first contribution of this thesis is an innovative use of the agglomerative hierarchical clustering algorithm to classify similar travels in terms of their origin and destination, together with the proposal for a methodology to analyse drivers’ route choice behaviour using GPS coordinates and optimal alternatives. The clusters of repetitive travels made by a sample of drivers are then analysed to compare observed route choices to the modelled alternatives. The results of the analysis show that drivers select routes that are more reliable but that are more expensive in terms of travel time. Successively, different types of users of a service that provides information on the real time arrivals of bus at stop are classified using Support Vector Machines. The results shows that the results of the classification of different types of bus transport users can be used to update or complement the census on bus transport flows. Finally, the problem of the classification of accidents made by two wheelers vehicles is presented together with possible future application of clustering methodologies aimed at identifying and classifying the different types of accidents.

Variation of vector quantisation and speech waveform coding

A new idea for waveform coding using vector quantisation (VQ) is introduced. This idea makes it possible to deal with codevectors much larger than before for a fixed bit per sample rate. Also a solution to the matching problem (inherent in the present context) in the &-norm describing a measure of neamess is presented. The overall computational complexity of this solution is O(n3 log, n). Sample results are presented to demonstrate the advantage of using this technique in the context of coding of speech waveforms.

Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.

Content Classification of Multimedia Documents using Partitions of Low-Level Features

Audio-visual documents obtained from German TV news are classified according to the IPTC topic categorization scheme. To this end usual text classification techniques are adapted to speech, video, and non-speech audio. For each of the three modalities word analogues are generated: sequences of syllables for speech, “video words” based on low level color features (color moments, color correlogram and color wavelet), and “audio words” based on low-level spectral features (spectral envelope and spectral flatness) for non-speech audio. Such audio and video words provide a means to represent the different modalities in a uniform way. The frequencies of the word analogues represent audio-visual documents: the standard bag-of-words approach. Support vector machines are used for supervised classification in a 1 vs. n setting. Classification based on speech outperforms all other single modalities. Combining speech with non-speech audio improves classification. Classification is further improved by supplementing speech and non-speech audio with video words. Optimal F-scores range between 62% and 94% corresponding to 50% - 84% above chance. The optimal combination of modalities depends on the category to be recognized. The construction of audio and video words from low-level features provide a good basis for the integration of speech, non-speech audio and video.

Assessing and mapping LD and SLM to directly support the planning and scaling up of SLM interventions to combat desertification

Global investment in Sustainable Land Management (SLM) has been substantial, but knowledge gaps remain. Overviews of where land degradation (LD) is taking place and how land users are addressing the problem using SLM are still lacking for most individual countries and regions. Relevant maps focus more on LD than SLM, and they have been compiled using different methods. This makes it impossible to compare the benefits of SLM interventions and prevents informed decision-making on how best to invest in land. To fill this knowledge gap, a standardised mapping method has been collaboratively developed by the World Overview of Conservation Approaches and Technologies (WOCAT), FAO’s Land Degradation Assessment in Drylands (LADA) project, and the EU’s Mitigating Desertification and Remediating Degraded Land (DESIRE) project. The method generates information on the distribution and characteristics of LD and SLM activities and can be applied at the village, national, or regional level. It is based on participatory expert assessment, documents, and surveys. These data sources are spatially displayed across a land-use systems base map. By enabling mapping of the DPSIR framework (Driving Forces-Pressures-State-Impacts-Responses) for degradation and conservation, the method provides key information for decision-making. It may also be used to monitor LD and conservation following project implementation. This contribution explains the mapping method, highlighting findings made at different levels (national and local) in South Africa and the Mediterranean region. Keywords: Mapping, Decision Support, Land Degradation, Sustainable Land Management, Ecosystem Services, Participatory Expert Assessment

Using citation data to improve retrieval from MEDLINE.

OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.

Family Preservation and Support: Past, Present, and Future

As the family preservation and support movement evolves rapidly, this article overviews the past, present and future of this approach to policy and services. Building upon several decades of practice experience and research, and now federally funded, program designers are searching for ways to implement system wide change with an array of services all from a family focus, and strengths perspective. Critical issues facing the movement are discussed and a set of benchmarks to judge our future success is presented.

Impaired top-down modulation of saccadic latencies in patients with schizophrenia but not in first-degree relatives

Impaired eye movements have a long history in schizophrenia research and meet the criteria of a reliable biomarker. However, the effects of cognitive load and task difficulty on saccadic latencies (SL) are less understood. Recent studies showed that SL are strongly task dependent: SL are decreased in tasks with higher cognitive demand, and increased in tasks with lower cognitive demand. The present study investigates SL modulation in patients with schizophrenia and their first-degree relatives. A group of 13 patients suffering from ICD-10 schizophrenia, 10 first-degree relatives, and 24 control subjects performed two different types of visual tasks: a color task and a Landolt ring orientation task. We used video-based oculography to measure SL. We found that patients exhibited a similar unspecific SL pattern in the two different tasks, whereas controls and relatives exhibited 20–26% shorter average latencies in the orientation task (higher cognitive demand) compared to the color task (lower cognitive demand). Also, classification performance using support vector machines suggests that relatives should be assigned to the healthy controls and not to the patient group. Therefore, visual processing of different content does not modulate SL in patients with schizophrenia, but modulates SL in the relatives and healthy controls. The results reflect a specific oculomotor attentional dysfunction in patients with schizophrenia that is a potential state marker, possibly caused by impaired top-down disinhibition of the superior colliculus by frontal/prefrontal areas such as the frontal eye fields.

Brain-borne IL-1 adjusts glucoregulation and provides fuel support to astrocytes and neurons in an autocrine/paracrine manner.

It is still controversial which mediators regulate energy provision to activated neural cells, as insulin does in peripheral tissues. Interleukin-1β (IL-1β) may mediate this effect as it can affect glucoregulation, it is overexpressed in the 'healthy' brain during increased neuronal activity, and it supports high-energy demanding processes such as long-term potentiation, memory and learning. Furthermore, the absence of sustained neuroendocrine and behavioral counterregulation suggests that brain glucose-sensing neurons do not perceive IL-1β-induced hypoglycemia. Here, we show that IL-1β adjusts glucoregulation by inducing its own production in the brain, and that IL-1β-induced hypoglycemia is myeloid differentiation primary response 88 protein (MyD88)-dependent and only partially counteracted by Kir6.2-mediated sensing signaling. Furthermore, we found that, opposite to insulin, IL-1β stimulates brain metabolism. This effect is absent in MyD88-deficient mice, which have neurobehavioral alterations associated to disorders in glucose homeostasis, as during several psychiatric diseases. IL-1β effects on brain metabolism are most likely maintained by IL-1β auto-induction and may reflect a compensatory increase in fuel supply to neural cells. We explore this possibility by directly blocking IL-1 receptors in neural cells. The results showed that, in an activity-dependent and paracrine/autocrine manner, endogenous IL-1 produced by neurons and astrocytes facilitates glucose uptake by these cells. This effect is exacerbated following glutamatergic stimulation and can be passively transferred between cell types. We conclude that the capacity of IL-1β to provide fuel to neural cells underlies its physiological effects on glucoregulation, synaptic plasticity, learning and memory. However, deregulation of IL-1β production could contribute to the alterations in brain glucose metabolism that are detected in several neurologic and psychiatric diseases.Molecular Psychiatry advance online publication, 8 December 2015; doi:10.1038/mp.2015.174.